Can the RTX 5090 D run Qwen3.5 9B?

Yes. Qwen3.5 9B fits the RTX 5090 D's 32 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 171 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
32 GB
Needs at Q4
7.8 GB
Headroom
24.2 GB
Speed (Q4_K_M)
~171 tok/s

Every quantization

Qwen3.5 9B (9.7B) on 32 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M6 GB7.8 GBFits easily~171 tok/s
Q5_K_M7 GB8.8 GBFits easily~147 tok/s
Q8_010 GB11.8 GBFits easily~103 tok/s
FP1619 GB20.8 GBFits~54 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 5090 D's 1790 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf unsloth/Qwen3.5-9B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3.5 9B details →

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Qwen3.5 9B on other hardware

Also runs on the RTX 5090 D

Frequently asked

Quick answers

Can the GeForce RTX 5090 D run Qwen3.5 9B?

Yes. Qwen3.5 9B fits the RTX 5090 D's 32 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 171 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Qwen3.5 9B need?

About 6 GB of weights at Q4_K_M, 10 GB at Q8_0 and 19 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3.5 9B on the RTX 5090 D?

Roughly 171 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5090 D's 1790 GB/s memory bandwidth.